Every piece of data that exists about a person, their behaviour, and any prediction has a half-life. Relevance decays over time.
Name, for example, might have a half-life of about a fifty years or so. In a hundred years I’ll be dead and my name will only be half as relevant as it was fifty years before when I was alive. My search history could have a half-life of between two weeks and two minutes. If I’m trying to find my nearest petrol station, the chances are that the results are most relevant between now and when I put some fuel in my car, and ten minutes later the relevance has halved and is only useful for agregating with other behavioural data. A year’s worth of transactional data about what I’ve bought each of the past fifty two weeks might have a half-life of five years if my purchase habits stay the same, but as those habits are likely to change over time the data set would also change over time with certain points decaying faster than others if I stopped buying certain items.
Understanding how each piece of data has it’s own half-life and how the relevance decays over time based on that half-life can help companies provide better personalisation and could be a means of deciding when data should be deleted to conform with evolving data protection regulations.